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Knowledge in Computer Science (CS) is essential, and companies have increased their demands for CS professionals. Despite this, many jobs remain vacant. Furthermore, computational thinking (CT) skills are required in all contexts of problem solving. A further serious problem arises from the gender disparity in technology related fields. Even if tech companies want to hire women in technology, the number of women who enter these fields is remarkably low. In high schools with no technical focus, most teenagers acquire only low-level skills in CS. The consequences are misleading preconceptions about the fundamental ideas of CS and stereotype-based expectations. Consequently, many teenagers exclude computing from their career path. In this paper, two promising concepts to overcome these challenges are presented. In 2018, a voluntary gamified lecture "Design your own app", held at the University of Graz for students of all degree programs, was introduced. The course attracted over 200 students and received positive evaluations. This led to the second concept. In January 2019, a MOOC (Massive Open Online Course) with the title "Get FIT in Computer Science" was designed and launched in August 2019 on the platform iMooX.at with the goal to provide a basic introduction to different concepts of CS, including programming and the application of game design strategies. The MOOC was accompanied by an offline lecture, following the principles of flipped classroom and inverse blended learning. For evaluation purposes, we collected data at three stages: 1) during the MOOC, 2) during the offline lecture, and 3) two months after the lecture. The results showed that the MOOC framework was a promising approach to support and motivate at least a certain group of first-semester students, especially those who had no prior knowledge in CS.

Background: Key to optimizing Computational Thinking (CT) instruction is a precise understanding of the underlying cognitive skills. Román-González et al. (2017) reported unique contributions of spatial abilities and reasoning, whereas arithmetic was not significantly related to CT. Disentangling the influence of spatial and numerical skills on CT is important, as neither should be viewed as monolithic traits. Objective: This study aimed (1) to replicate the results of a previous study by Román-González et al. (Computers in Human Behaviour 72), and (2) to extend this research by investigating other theoretically relevant constructs. Specifying the contribution of reasoning (i.e. numerical, figural), numerical skills (i.e. arithmetic, algebra), and spatial skills (i.e. visualization, mental rotation, short-term memory) helps to better understand the cognitive mechanisms underlying CT. Method: We investigated a sample of 132 students from Grades 7-8 (age range 12-15 years). Participants completed the Computational Thinking test, as well as a variety of psychometric assessments of reasoning, numerical, and spatial skills. To determine which cognitive skills are relevant for CT, we calculated bivariate correlations and performed a linear regression analysis. Findings: Results confirmed unique contributions of figural reasoning and visualization. Additional variance was explained by algebraic skills. Implications: We conclude that CT engages cognitive mechanisms extending beyond reasoning and spatial skills.

Pocket Code is a visual programming-based mobile application for creating games, animations, music, videos, and other types of applications. This paper presents the integration of face recognition capabilities into Pocket Code through a visual programming interface based on the FaceNet architecture. The FaceNet dataset is used to train and deploy a face recognition model in Pocket Code visual programming. Integration of the FaceNet algorithm into Pocket Code aims to enhance the accessibility and simplicity of facial recognition technology for students and developers. Building face recognition applications typically involves writing complex code, which can be challenging for both beginners and experienced developers. The Pocket Code visual programming blocks have simplified the process, enabling anyone to easily incorporate face detection into their projects, irrespective of their coding experience. The article discusses the implementation and performance assessment of the FaceNet algorithm in Pocket Code visual programming. [ABSTRACT FROM AUTHOR]

A neural network is a machine learning (ML) program or model that processes information and recognizes patterns, similar to the human brain. The neural network algorithm operates by training on data to learn and enhance its accuracy. If there is any mistake in the learning process, the machine will react unexpectedly and produce incorrect information. So, whenever a neural network model is developed, it is mandatory to evaluate its performance and ensure its output is accurate. Cameras, touchscreens, internet connectivity, and powerful CPUs have contributed to the popularity of smartphones. Mobile apps are software applications that run on smartphones. ML enhances mobile app functionality by offering features such as voice recognition, image analysis, natural language processing, personalization, and recommendations. Training ML models using mobile apps is challenging due to limited resources, data, and privacy concerns. Object recognition is a neural network-based technique that enables the identification and localization of objects in an image or video. This technique is used in driverless cars, disease identification, industrial inspection, robotics, and more. In this paper, the author introduces a new neural network-based algorithm that utilizes mobile devices for training and recognizing images. Although mobile devices have some technological limitations for training, well-established guidelines for systematically mapping verification and validation techniques have been used in the proposed neural network to ensure performance and correctness in on-device object training and recognition. [ABSTRACT FROM AUTHOR]

Research suggests that girls' initial interest in computer science tends to decline during their teenage years, a trend that is not observed among boys. This paper addresses this gender gap and proposes integrating programming into handicraft lessons to provide a creative activity for all students. The project was conducted in three Austrian schools over one year (2019-2020), involving 229 middle school students. The evaluation included questionnaires and the assessment of programmed and stitched designs to structure gender-sensitive workshops. While boys consistently reported higher scores than girls in interest, sense of belonging, and enjoyment, girls' scores remained more stable. However, girls were significantly more likely to express pride in their final designs than boys, and overall, more girls completed individual designs as final products. These findings can be applied to interdisciplinary handicraft lessons in line with the Maker-Education movement to foster interest in programming. [ABSTRACT FROM AUTHOR]

Sharing information with the public is becoming easier than ever before through the usage of the numerous social media platforms readily available today. Once posted online and released to the public, information is almost impossible to withdraw or delete. More alarmingly, postings may carry sensitive information far beyond what was intended to be released, so-called incidental data, which raises various additional security and privacy concerns. To improve our understanding of the awareness of incidental data, we conducted a survey where we asked 192 students for their opinions on publishing selected postings on social media. We found that up to 21.88% of all participants would publish a posting that contained incidental data that two-thirds of them found privacy-compromising. Our results show that continued efforts are needed to increase our awareness of incidental data posted on social media. [ABSTRACT FROM AUTHOR]

Privacy and security require not only strong algorithms but also reliable and readily available sources of randomness. To tackle this problem, one of the causes of single-event upsets is the utilization of a non-deterministic entropy source, specifically ultra-high energy cosmic rays. An adapted prototype based on existing muon detection technology was used as the methodology during the experiment and tested for its statistical strength. Our results show that the random bit sequence extracted from the detections successfully passed established randomness tests. The detections correspond to cosmic rays recorded using a common smartphone during our experiment. Despite the limited sample, our work provides valuable insights into the use of ultra-high energy cosmic rays as an entropy source. [ABSTRACT FROM AUTHOR]

A set of move types that give rise to a composite neighborhood relation for general workforce scheduling process is proposed. Basic prohibition mechanisms of tabu search are used in the move selection process.

The performance of four improvement methods for schedule optimization were compared. The comparison was made using data for generating operations schedules for the steel making plant of VOEST Apline Stahl AG in Linz, Austria. The four, which execute local search in different ways, are iterative deepening, random search, tabu search and genetic algorithms. All four apply a heuristic to determine the degree to which explicitly represented constraints are satisfied.

The rapid advancement of mobile computing technology and the rising usage of mobile apps made our daily life more productive. The mobile app should operate all the time bug-free in order to improve user satisfaction and offers great business value to the end user. At the same time, smartphones are full of special features that make testing of apps more challenging. Actually, the quality is a must for successful applications and it cannot be achieved without testing and verification. In this paper, we present the behavior driven development methodology and Cucumber framework to automate regression testing for Android apps. Particularly, the proposed methods use the visual programming language for smartphones (Catrobat) as a reference. The Catrobat program scripts communicate via a broadcast mechanism. The objective is to test the broadcast mechanism from different angles and track regression errors as well as specify and diagnose bugs with the help of executable specifications. The results show that the methods are able to effectively reveal deficiencies in the broadcast mechanism, and ensure that the app meets end users' expectations and needs. [ABSTRACT FROM AUTHOR]

We consider vertex colorings of graphs in which adjacent vertices have distinct colors. A graph is s-chromatic if it is colorable in s colors and any coloring of it uses at least s colors. The forcing chromatic number Fχ(G) of an s-chromatic graph G is the smallest number of vertices which must be colored so that, with the restriction that s colors are used, every remaining vertex has its color determined uniquely. We estimate the computational complexity of Fχ(G) relating it to the complexity class US introduced by Blass and Gurevich [Inform. Control, 55 (1982), pp. 80-88]. We prove that recognizing whether Fχ(G) ≤ 2 is US-hard with respect to polynomial-time many-one reductions. Moreover, this problem is coNP-hard even under the promises that Fχ(G) ≤ 3 and G is 3-chromatic. On the other hand, recognizing whether Fχ(G) ≤ k, for each constant k, is reducible to a problem in US via a disjunctive truth-table reduction. Similar results are obtained also for forcing variants of the clique and the domination numbers of a graph. [ABSTRACT FROM AUTHOR]

Provides an overview of the system used for the design of shifts and assignments of employees from the research project called Rota. Stages of workforce rotation; Determination of temporal requirements and total number of employees needed; Integration of techniques from management sciences and artificial intelligence to solve shifting problems.